ERDC/GRL TR-14-1
Creating Orthographically Rectified Satellite
Multi-Spectral Imagery with High Resolution
Digital Elevation Model from LiDAR
A Tutorial
Geospatial Research Laboratory
Roger O. Brown
Approved for public release; distribution is unlimited.
August 2014
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ERDC/GRL TR-14-1
August 2014
Creating Orthographically Rectified Satellite
Multi-Spectral Imagery with High Resolution
Digital Elevation Model from LiDAR
A Tutorial
Roger O. Brown
Geospatial Research Laboratory (GRL)
U.S. Army Engineer Research and Development Center
7701 Telegraph Road
Alexandria, VA 22135-3864
Final Report
Approved for public release; distribution is unlimited.
Prepared for
Headquarters, U.S. Army Corps of Engineers
Washington, DC 20314-10009
ERDC/GRL TR-14-1
Abstract
Orthoimages are used to produce image-map products for navigation and
planning, and serve as source data for advanced research, development,
testing, and evaluation of feature extraction methods. This tutorial describes procedures for making orthoimages from Light Detection and
Ranging (LiDAR) Digital Elevation Models (DEM) and from commercial
satellite Multi-Spectral Imagery (MSI) in the National Imagery Transmission Format (NITF) with Rational Polynomial Coefficients (RPC).
Orthoimages rectify digital imagery to remove geometric distortions
caused by the varying elevations of the exposed terrain features, and by
exterior and interior orientations of the sensor. When orthoimages are
combined, the resulting mosaic covers a wider area and contains less
visible seams, which makes the map easier to understand. RPC replace the
actual sensor model while processing the original MSI. This generic replacement sensor model is provided with the distributed imagery to simplify the process of removing the geometric distortions so image processing software can create orthoimages without using the actual sensor
model, which is often not provided. The DEM and MSI also become better
registered together after producing the orthoimage by using the RPC. This
assists feature extraction and segmentation when the DEM is added as
extra data bands to the MSI.
DISCLAIMER: The contents of this report are not to be used for advertising, publication, or promotional purposes.
Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products.
All product names and trademarks cited are the property of their respective owners. The findings of this report are not to
be construed as an official Department of the Army position unless so designated by other authorized documents.
DESTROY THIS REPORT WHEN NO LONGER NEEDED. DO NOT RETURN IT TO THE ORIGINATOR.
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Contents
Abstract ..........................................................................................................................................................iv
Illustrations ....................................................................................................................................................vi
Preface ......................................................................................................................................................... viii
1
Introduction ............................................................................................................................................ 1
1.1
1.2
1.3
1.4
Reasons for orthorectification ....................................................................................... 1
DEM derived from LiDAR ................................................................................................ 2
Comparing LiDAR DEM and MSI resolutions ................................................................. 3
Methods of orthorectification ........................................................................................ 4
1.4.1 General process of orthorectification ........................................................................................ 4
1.4.2 Sensor models that use RPC...................................................................................................... 4
1.4.3 Orthoimage mosaics ................................................................................................................... 5
1.4.4 Tutorial software ......................................................................................................................... 5
2
Procedures ............................................................................................................................................. 6
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
3
Summary ......................................................................................................................... 6
Description of DEM and MSI data ................................................................................. 8
Tutorial overview ........................................................................................................... 14
Viewing DEM and MSI data .......................................................................................... 14
Mosaic LiDAR DEM ....................................................................................................... 21
Adjust the terrain heights from the LiDAR DEM .......................................................... 25
Convert LiDAR DEM from UTM to geographic decimal degrees ................................. 33
Making orthoimages ..................................................................................................... 35
Create UTM orthoimage ............................................................................................... 41
Conclusions and Recommendations ..............................................................................................46
3.1 Conclusions ................................................................................................................... 46
3.2 Recommendations........................................................................................................ 46
References ...................................................................................................................................................48
Acronyms and Abbreviations ....................................................................................................................50
Appendix A: LiDAR DEM Raster ...............................................................................................................51
Appendix B: Orthoimage Spatial Resolution .........................................................................................53
Appendix C: RPC Projective Equations...................................................................................................68
Appendix D: Orthoimage Mosaic .............................................................................................................69
Appendix E: Earth Gravity Model Terrain Heights ................................................................................70
Report Documentation Page (SF 298) ...................................................................................................71
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Illustrations
Figures
2-1
Orthoimage production flowchart ................................................................................................ 7
2-2
LiDAR terrain heights elevation grids beneath WorldView-2 MSI ............................................ 9
2-3
DEM and MSI folder plus files data structure ..........................................................................10
2-4
LiDAR DEM raster files (*.tif) ......................................................................................................11
2-5
MSI files (*.ntf)..............................................................................................................................12
2-6
WorldView-2 image frames mismatch before making orthoimages .....................................13
2-7
Open raster layer into viewer.......................................................................................................15
2-8
Show pathname for layer to add ................................................................................................16
2-9
Select layer to add ........................................................................................................................16
2-10
Pick image frame to view ............................................................................................................ 17
2-11
Fit entire image into viewer .........................................................................................................18
2-12
Entire image fit into viewer ..........................................................................................................19
2-13
DEM and MSI View .......................................................................................................................20
2-14
Begin mosaic of LiDAR DEM raster ............................................................................................ 21
2-15
MosaicPro “Add Images..” ...........................................................................................................22
2-16
Pick terrain heights elevation grids ............................................................................................22
2-17
Restack terrain height elevation grid tiles .................................................................................23
2-18
Run mosaic process .....................................................................................................................24
2-19
Show output filename from mosaic process ............................................................................ 24
2-20
Process list mosaic completion ..................................................................................................25
2-21
LiDAR terrain heights elevation mosaic.....................................................................................26
2-22
Terrain heights elevation metadata ........................................................................................... 27
2-23
Choosing vertical datum for terrain elevations ........................................................................ 27
2-24
Review projection metadata for MSI ..........................................................................................28
2-25
MSI projection and datum...........................................................................................................29
2-26
Recalculate terrain heights elevation values ............................................................................30
2-27
Define output elevation vertical datum .....................................................................................30
2-28
Project terrain heights from EGM96 to WGS84 vertical datum............................................. 31
2-29
Convert WGS84 terrain heights from EGM96 to WGS84 ....................................................... 31
2-30
Filename output for WGS84 terrain heights from EGM96 .....................................................32
2-31
Convert terrain heights from EGM96 to WGS84......................................................................32
2-32
Close processing list to complete conversion ...........................................................................33
2-33
Start projection of DEM raster from UTM to geographics .......................................................33
2-34
Settings for reprojected images .................................................................................................34
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2-35
Close completed reprojection process ......................................................................................35
2-36
Begin the orthoimage process ...................................................................................................35
2-37
Pick NITF filename for making orthoimage ..............................................................................36
2-38
Show RPC model ..........................................................................................................................36
2-39
Show DEM raster .......................................................................................................................... 37
2-40
Start making the orthoimage.....................................................................................................38
2-41
Close completed process for making orthoimage ...................................................................38
2-42
Overlapping MSI before making orthoimages ..........................................................................39
2-43
Overlapping MSI after making both orthoimages ....................................................................40
2-44
Resample geographic MSI orthoimage to UTM projection .....................................................42
2-45
Reproject images menu ..............................................................................................................43
2-46
Processing list reproject progress ..............................................................................................44
2-47
Orthoimage alignment with LiDAR terrain heights image.......................................................45
A-1
Motivation for initial step of waveform post-processing .......................................................... 51
B-1
MSI and orthoimage equivalent spatial resolutions................................................................ 57
B-2
Default orthoimage pixel GSD In decimal degrees ..................................................................58
B-3
Default orthoimage pixel GSD in meters ...................................................................................59
B-4
Raw MSI corners from NITF ........................................................................................................60
B-5
Sensor orientation metadata from NITF.................................................................................... 61
B-6
1 m GSD Orthoimage Compared To 1 m GSD DEM Raster ...................................................62
B-7
Raw MSI and DEM raster terrain height posts .........................................................................64
B-8
Orthoimage with nominal GSD of raw MSI and DEM posts ...................................................65
B-9
1 m GSD orthoimage compared x:3.09 by y:2.63 GSD orthoimage.....................................66
E-1
WGS84 ellipsoid height ...............................................................................................................70
E-2
EGM96 geoid height ....................................................................................................................70
Tables
1-1
DEM and WorldView-2 MSI resolutions ....................................................................................... 3
B-1
DEM and MSI pixel dimensions..................................................................................................55
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Preface
This study was conducted for the U.S. Army Geospatial Research Laboratory under the “Data Level Enterprise Tools” project. The technical monitors were Michael Campbell (TR-S), Brian Graff (TR-G), and James
Eichholtz (TR-S).
The work was performed by the Data Signature Analysis Branch (CEERDTR-S) of the Topographic Research Division (CEERD-TR), U.S. Army
Engineer Research and Development Center, Geospatial Research Laboratory (ERDC-GRL). At the time of publication, Jennifer Smith was the
Branch Chief, CEERD-TR-S; Dr. Eric Zimmerman was the Division Chief,
CEERD-TR; and Ritch Rodebaugh, CEERD-TV-T, was the Technical
Director for overseeing the CEERD-TR Division. The process described in
this document was tested by Alana Hubbard and Chris D’Errico from the
U.S. Army Geospatial Center (AGC) Imagery Office, and by the AGC Military Support Team of SFC Roger Adkins and SGT Clay Weaver. The Topographic Research Division works closely with Dr. Joseph Fontanella,
Director of the U.S. Army Geospatial Center and the Geospatial Research
Laboratory (CEERD-GRL).
The Commander and Executive Director of ERDC is COL Jeffrey R. Eckstein, and the Director for Research and Development plus Chief Scientist
of ERDC is Dr. Jeffery P. Holland.
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1
Introduction
This Introduction section covers the following topics:
1. The reasons for orthorectification – why it is done.
2. Digital Elevation Models (DEM) that are derived from Light Detection and
Ranging (LiDAR).
3. Comparing LiDAR DEM and WorldView-2 Multi-Spectral Imagery (MSI)
Ground Sample Distance (GSD) resolutions.
4. The methods of orthorectification – how it is done.
5. Sensor models that use Rational Polynomial Coefficients (RPC).
Chapter 2, “Procedures,” describes the step-by-step process for using the
DEM, MSI and RPC to construct an orthoimage. The appendixes include
supplemental detail.
1.1
Reasons for orthorectification
The Army Geospatial Center and the Engineer Research and Development
Center, Geospatial Research Laboratory (ERDC-GRL) supplies orthoimage
mosaics that are products to aid tactical decisions and improve situational
awareness during military operations (HQDA, 2001; HQUSACE, 2002).
The orthorectification process is a practical way to register the DEM and
raw MSI together for synergistic analysis of both datasets.
Raw satellite or aerial imagery has distortions regarding the positional
accuracy of its coordinates because the nearly vertical overhead imagery
contains horizontal displacement based on the physical terrain. Angular
distortions can occur from the perspective views of the sensor as it looks at
the terrain, and distortions may occur in completely flat areas (Agouris, et
al., 2004; Miller, 2013; Scarpace, 2013). The position and angular attitudes of the sensor are commonly called its “exterior orientation.” The
position of the sensor also changes along its flight path while scanning the
terrain so that other distortions occur while the sensor scans the terrain,
including distortions caused by: (1) the relationship between the focal
center and the image plane of the sensor, and (2) the electrical or mechanical scanning motion of the sensor array that measures the energy reflected or emitted from the terrain.
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Measurements of location in this raw imagery are inaccurate unless these
displacements and distortions are first removed. Removing these distortions also facilitates the projection of the initially constructed planimetric
orthoimage from its geocentric latitude and longitude coordinates into
other map projections, say Universal Transverse Mercator (UTM) for
example. These distortions are best described by the mathematics and
projective geometry of photogrammetry (Forstner & Wrobel, 2013;
Mugnier, et al., 2013).
This raw overhead imagery may be corrected by removing the combined
displacements and distortions through a process called “orthorectification,” which results an orthographically rectified image commonly called
an “orthoimage.” Making orthoimages is a complex process that requires:
(1) a DEM of the terrain heights, (2) imagery from an airborne or satellite
sensor, and (3) a sensor model for projective equations from the exposed
terrain to the sensor image.
The accuracy of the orthoimage is limited by the vertical accuracy of the
DEM, and by the accuracy of the sensor model describing its exterior and
interior orientations, when exposing the image of the terrain. A cursory
check (or a more detailed analysis) of its accuracy should be done to access
the quality of the orthoimage. Note that this tutorial gives more attention
to the rendering aspects of orthoimages.
1.2
DEM derived from LiDAR
A DEM can be a uniform raster of terrain heights. The DEM can come
from a variety of sources, and they are available at many resolutions.
LiDAR is a source for a DEM with high resolution. Appendix A includes an
explanation of how LiDAR works. The unique aspect of this tutorial is that
it uses a DEM derived from LiDAR, which has better spatial resolution
than the MSI, as the input for orthorectification. The use of a DEM derived
from Buckeye LiDAR offers advantages over using a more traditional
DEM:
1. A DEM derived from Buckeye LiDAR has high resolution that is prepared for
the orthorectification of Buckeye imagery, and that often has a spatial resolution in the range of 0.5 to 1.0m. The LiDAR DEM is thus extremely useful for
orthorectification because it has better spatial resolution compared to the
WorldView-2 commercial satellite MSI used in this tutorial.
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3
2. Buckeye LiDAR is now collected simultaneously along with Buckeye, so
there is little temporal discrepancy between the LiDAR DEM and MSI.
3. It is easier to orthorectify Buckeye imagery with Buckeye LiDAR because
they are nominally registered together when simultaneously scanning the
terrain before the LiDAR cloud is converted to a DEM raster.
4. The accuracy and precision of the LiDAR from Buckeye is very good. The
Buckeye LiDAR vertical accuracy with combined systematic and random
error is better than 0.5 m.
1.3
Comparing LiDAR DEM and MSI resolutions
Table 1-1 lists the nominal spatial and spectral resolutions between the
Buckeye LiDAR DEM and the MSI data that are used to combine both
datasets. The high resolution MSI used for this tutorial is WorldView-2.
The WorldView-2 imagery contains eight bands and has a spatial resolution or GSD of approximately 2 m. The GSD is defined as the width or
length of an image picture element (pixel) projected onto the terrain surface, commonly called a pixel footprint. These are approximate nominal
values for the MSI GSD (LANDinfo Worldwide Mapping, 2014; Satellite
Imaging Corporation, 2013). However, the actual GSD for each pixel footprint throughout the MSI depends on the terrain heights and on the orientation of the sensor while it scans the terrain. The spectral resolution of
MSI is expressed as bands with a range of wavelengths for the measured
energy that is incident onto pixels of each layer.
Table 1-1. DEM and WorldView-2 MSI resolutions.
ERDC/GRL TR-14-1
Many options exist regarding the output spatial resolution GSD in each of
the X-East and Y-North directions for the orthoimage. One option uses the
default output GSD values provided by the software during the process of
making each orthoimage. It is unnecessary to determine another GSD that
is common to all separately produced orthoimages. However, the raw MSI
still is resampled when producing an orthoimage because of the X-East
and Y-North orientation for an orthoimage compared along the Xdirection and across the Y-direction of the flight track within the raw MSI
space. Appendix B describes other options for the produced orthoimage
GSD. Note that this tutorial uses the output GSD for each orthoimage
suggested by the software that reflects the nominal spatial resolution of
the raw MSI. The tutorial does not use the two optional steps that are
mentioned, but not used, in the Chapter 2, “Procedures” (see p 37).
1.4
Methods of orthorectification
1.4.1 General process of orthorectification
The general process of making orthoimages uses a sensor model with
projective equations to find the nearest MSI pixel that matches the X, Y,
and Z-elevation value from the DEM raster, or, it uses interpolated terrain
heights from the DEM raster when there is a mismatch between output
orthoimage GSD and the DEM raster GSD. These are commonly called
“projective” equations because they describe the sensor line-of-sight when
each pixel within its imagery is exposed. These equations, which project
from the DEM to the MSI, contain parameters that express the exterior
and interior orientations of the sensor when each pixel is exposed.
1.4.2 Sensor models that use RPC
RPC can replace using the actual sensor model to find the MSI pixel that is
a function of the X, Y, and Z values from the DEM raster. Appendix C
describes the RPC concept in more detail. These RPC are used in place of
the actual sensor model with its projective equations that know the exterior and interior orientations for the sensor plus its platform (Forstner, et
al., 2013; Hu, et al., 2004; Jacobsen, 2008; Tao & Hu, 2001). WorldView2 MSI often provides RPC as metadata. RPC can be used when the actual
sensor model information is unavailable to the user. It is easier to incorporate a generic RPC replacement sensor model into the image processing
software that is making the orthoimages, compared to using the greater
complexity and the proprietary content of the actual sensor model. These
RPC replace the actual sensor model for ground-to-image transforms with
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a single set of parameters for each segment of WorldView-2 MSI. It is
possible to use the RPC for producing an orthoimage when there is a DEM
in a raster format that covers the same area as the MSI. The entire
orthorectification process reduces visible mismatches between overlapping
orthoimages regardless of using the RPC or the actual sensor model.
1.4.3 Orthoimage mosaics
The process to create a mosaic of orthoimages ensures that a collection of
overlapping adjacent orthorectified image frames cover a larger area,
where the seams between them are unseen when the frames are combined
into one image. The orthoimage mosaic process is unnecessary if a single
orthoimage covers the entire area of interest. Chapter 2 of this tutorial,
“Procedures,” describes the whole process of making each orthoimage.
Appendix D briefly describes a separate process to combine image frames
into an orthoimage mosaic. The process outlined in this tutorial describes
two overlapping, but separately produced orthoimages. This tutorial ignores tonal imbalance and output GSD discrepancies between
orthoimages in a mosaic. Two remaining concerns that will be addressed
in future efforts are: (1) Many customers, including those doing MSI feature extraction from the orthoimages, consider it unacceptable when the
tonal imbalance forms a “quilted patchwork” in the orthoimage mosaic,
and (2) The mosaic of orthoimages with different GSD can be problematic.
1.4.4 Tutorial software
Except for a process where the LiDAR point cloud is converted to a DEM
raster by software, this tutorial also uses a single image processing product
and it does not combine results from more than one software package.
This tutorial produces orthoimages solely with ERDAS Imagine 2011
commercial imagery processing software, which can process the National
Imagery Transmission Format (NITF) with an RPC sensor model when
making the orthoimages using a DEM raster. NITF is a standard that
provides a detailed description of the overall file structures for formatting
and exchanging digital imagery. The NITF contains supporting metadata
to describe the image data and the products related to it (NITF Standard
Technical Board, 2007). Other commercial imagery processing software
can produce orthoimages, but their processing steps might vary slightly
from what is described in this tutorial when commercial satellite MSI
exists with RPC, for example, WorldView-2 in the NITF. Nevertheless, the
processing steps described here can be adapted to other imagery processing systems that contain the RPC sensor model.
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2
Procedures
2.1
Summary
This chapter describes the steps necessary for making an orthoimage with a
LiDAR DEM raster and the RPC sensor model that comes with WordView-2
MSI. The steps outlined here suggest the processing steps for making
orthoimages from other DEM and MSI data besides LiDAR and WorldView2, if RPC come with the MSI. From this process, it is also possible to extrapolate general procedures needed to create orthimages using commercial
image processing software besides ERDAS Imagine 2011.
Figure 2-1 shows the start-to-finish production flowchart of the main
processes to create an orthoimage with the LiDAR DEM raster and the
Worldview-2 commercial satellite MSI, after the single mosaic for the
entire set of LiDAR DEM raster tiles has been formed. Equation 2-1, which
uses the notation shown in Figure 2-1, denotes how the brightness value
(𝒃𝒊 ) for an orthoimage grid-cell from the nearest pixel is found in each MSI
layer-𝒊 by the sensor model (→):
(𝒙, 𝒚, 𝒛, 𝒃𝒊 )𝑮𝒆𝒐−𝑾𝑮𝑺𝟖𝟒 = (𝒙, 𝒚, 𝒛)𝑳𝒊𝑫𝑨𝑹 → (𝒙′ , 𝒚′ , 𝒃𝒊 )𝑴𝑺𝑰
(2-1)
Appendix C details the photogrammetry mathematics and projective
equations of the sensor model. The remaining sections in this chapter
contain information and stepped processes for making an orthoimage.
Section 2.2 describes the suggested file structure of the LiDAR DEM raster, and of the MSI, for the processing described in this tutorial.
Section 2.3 describes how to load the DEM and MSI into the ERDAS
Imagine viewer for inexperienced image processing software users. The
instructions for subsequent subsections presume that the user can load
data into the viewer to keep track of processing results. Note that many
steps can be applied without the images loaded into the viewer. When
images must be loaded into the viewer to complete subsequent processing
steps, an additional “Load … image into viewer” step will be added to each
set of instructions.
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7
Figure 2-1. Orthoimage production flowchart.
Section 2.5 describes how to combine separate LiDAR DEM raster tiles
into one mosaic because the image processing software and the production
flowchart presumes that there is a single DEM raster that covers the entire
ground footprint of each MSI frame.
Section 2.6 describes the “Adjust LiDAR DEM” process to shift the entire
DEM raster mosaic of its UTM terrain height values from the Earth Gravity Model 1996 (EGM96) datum* into the World Geodetic System 1984. †
Note that the software incorrectly interprets LiDAR DEM datum as
WGS84 instead of EGM96. This misperception must be corrected by
adjusting the terrain height values in the DEM raster. Appendix E includes
an example that shows why the terrain height values within the DEM
raster should be converted from the EGM96 to the WGS84 datum expected by the RPC for the MSI, because large differences in respective
terrain height values between the two datums can propagate errors during
orthorectification.
*EGM96
refers to the equipotential gravity field depicting mean-sea-level across the Earth that is
commonly called the geoid.
†WGS84 refers to an earth-centered ellipsoid of revolution coordinate system for projective equations to
satellites.
ERDC/GRL TR-14-1
Section 2.7 describes transformation of the UTM projection, with its adjusted terrain height values, to the projection of geographic longitude and
latitude decimal degrees expected by the RPC sensor model.
Section 2.8 completes the process of creating the orthoimage from the
converted LiDAR DEM raster and from the raw MSI with the RPC sensor
model.
Section 2.9 describes the process to project the produced orthoimage from
geographic longitude and latitude decimal degrees into a desired coordinate
system for example UTM meters, which was the same projection of the
original LiDAR DEM raster. The mosaic process for the separately produced
orthoimages is very similar to the process of merging multiple LiDAR DEM
tiles into one piece. Appendix D describes the orthoimage mosaic process as
similar to that for the DEM raster, so the same process may be used to
create a mosaic of orthimages with different GSD resolutions.
2.2
Description of DEM and MSI data
Figure 2-2 shows the ERDAS Imagine viewer containing the NITF with
RPC for two overlapping frames of the WorldView-2 8-band commercial
satellite MSI. The LiDAR DEM is underneath the shown MSI. These LiDAR
DEM raster tiles and MSI data are the material used in this tutorial. The
quilted patchwork texture that characterizes the image of the LiDAR terrain
heights grids will disappear when the frames are joined into a single mosaic
of the elevations. The shape of every LiDAR DEM raster tile that spans the
same area as the MSI frames is shown with a blue outline.
The preparation of the LiDAR DEM raster is the most complex aspect of
making the orthoimage. To get correct results, the LiDAR must be converted to the same format and content expected by the MSI RPC, including
its projection plus horizontal and vertical datum. * This tutorial explains
how to do that next.
*
For help in gathering the DEM from LiDAR, or in gathering commercial satellite MSI with RPC to conduct
the process of making orthoimages, contact [email protected]
8
Figure 2-2. LiDAR terrain heights elevation grids beneath WorldView-2 MSI.
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10
Figure 2-3 shows the recommended file/folder organization for this task:
•
•
•
•
The “LiDAR” folder contains the LiDAR DEM rasters.
The “WV2_120501” and “WV2_120502” folders each contain one of
the two overlapping MSI frames.
The “LashkarGah_LiDAR_MSI.ixs” file and the
“LashkarGah_LiDAR_MSI” folder contain data that describe the
ERDAS Imagine “session” shown in Figure 2-2.
The “Afghan_LIDAR_IFSAR_IndexShapes_15Dec11” folder contains
the shapes of the boxes with blue outlines in the viewer.
Figure 2-3. DEM and MSI folder plus files data structure.
Figure 2-4 shows the LiDAR DEM raster files contained in the “LiDAR”
folder. Note that the “_a1_” in each “*.tif” filename indicates that these
files contain terrain heights from the first return of the LiDAR.
Figure 2-5 shows the MSI files contained in the “WV2_120501” folder, in
which the “*.ntf” file contains the MSI raster highlighted with a blue background. Other files in this folder contain metadata (including RPC) that
support further processing of the MSI to create its orthoimage. The
“WV2_120502” folder contains similar data for the second MSI frame
used in this tutorial.
Figure 2-6 shows the geometric mismatch between a pair of WorldView-2
MSI frames used in this tutorial, each of which is exposed one day apart.
This tutorial describes the construction of the northwest orthoimage. The
southeast orthoimage shown in later figures was made by using the same
process use to create the northwest orthoimage. Figure 2-7 shows that
most of the image mismatch between the two raw MSI (e.g., along the
dotted red line) is removed after making each orthoimage separately.
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Figure 2-4. LiDAR DEM raster files (*.tif).
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12
Figure 2-5. MSI files (*.ntf).
Figure 2-6. WorldView-2 image frames mismatch before making orthoimages.
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However, some geometric and spectral discrepancies remain after
orthorectification. Removing these remaining geometric discrepancies,
including tonal imbalance between both orthoimages, is a subject for
future research. Although this chapter does not explain the process for
forming the mosaic from separately produced orthoimages, it is similar to
the steps taken to form the LiDAR DEM mosaic described in Section 2.5.
Future basic or applied research should explore a method to remove the
remaining spatial and spectral discrepancies that remain in the displayed
overlapping orthoimages. Nevertheless, apparent spatial mismatch that
remains after making each orthoimage should still be less noticeable than
that seen between each overlapping frame of separate raw MSI frames
before their conversion to orthoimages.
2.3
Tutorial overview
This tutorial presumes that most users know how to load the DEM raster
and MSI data into the ERDAS Imagine viewer. Most image processing
steps can be applied without the data loaded into the viewer. Consequently, the instructions beyond Section 2-4 do not include the steps for loading
the data into the viewer, unless a “Load ... into the viewer” step is required.
The user is advised to use the viewer to review the image processing results before taking subsequent steps to ensure that the process has produced the desired results.
This following tutorial takes the form of a series of hands-on, three-part
exercises:
1. The short paragraph that begins the exercise explains the overall intent of
the following stepped exercise. The user is advised to read this introductory material before beginning the steps.
2. Each process is divided into numbered sequential steps.
3. The numbered steps are followed by figures that show the user how to
manipulate the software graphic user interface to follow each process.
Each numbered step is repeated in the figures to clearly show where the
user should take the indicated action.
2.4
Viewing DEM and MSI data
Figures 2-7 through 2-13 (Steps 1-8) show how to load a DEM Tagged
Image File Format (TIFF) raster frame into the ERDAS Imagine viewer.
To load the MSI NITF into the viewer, repeat Steps 1-8, but this time, in
Step 5, pick “NITF 2.x.” These steps also apply to viewing other DEM
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15
raster tiles and MSI frames. Presume that “press” or “select” means to
click the button on the left-hand side of the mouse. “Press-RHMB” means
click the button on right-hand side of the mouse that is a less frequent
action. The terms “press” or “select” were used in case the computer has a
touchscreen that you “tap” instead of the “click” of a mouse button. The
“Press-RHMB” instruction might be another action on a touchscreen.
1. Select “2D View #1” in the “Contents” subframe to open the dropdown list.
2. Select the “Open Raster Layer..” item from dropdown list.
3. Type the pathname into the “File name:” field of the “Select Layer to Add:”
menu.
4. Press button at right side of “Files of type:” dropdown to view the list.
5. Select “TIFF” from the file type list.
6. Select the filename of the desired image from the “Select Layer to Add:”
menu, then press “OK.”
7. Press-RHMB to select the filename in the “Contents” subframe to view the
popup list.
8. Select “Fit Layer To Window” from the popup list.
Figure 2-7. Open raster layer into viewer.
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Figure 2-8. Show pathname for layer to add.
Figure 2-9. Select layer to add.
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Figure 2-10. Pick image frame to view.
(6) Select the filename of the desired image from the
“Select Layer to Add:” menu, then press “OK.”
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Figure 2-11. Fit entire image into viewer.
(7) Press-RHMB to select the filename in the “Contents” subframe to view the popup list.
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Figure 2-12 shows the LiDAR DEM raster fit to the viewer window, in
which lighter pixels indicate higher terrain heights.
Figure 2-12. Entire image fit into viewer.
Figure 2-13 shows the results of repeating Steps 1-8 to load the MSI NITF
into the viewer. Presuming the same file and folder structures mentioned
in Section 2.3, type:
C:\Users\U4TRGROB\Documents\WaterResources\TestData\WV2_120501\
for the pathname in Step 3. Press “NITF 2.x” instead of “TIFF” in Steps 4-5
to find the:
12MAY01071532-M1BS-052716833010_01_P010.ntf
filename in Step 6. Drag the DEM filename above the MSI filename into
the Contents subframe, after loading MSI into the viewer, and then fit the
MSI to the viewer window to get view shown in Figure 2-13.
Figure 2-13. DEM and MSI View.
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2.5
21
Mosaic LiDAR DEM
Figures 2-14 through 2-16 show the process used to join the LiDAR DEM
grids into a single mosaic. Steps 1-5 initially populate the list of grids that
are part of the terrain heights mosaic. The viewer will show the LiDAR
DEM tiles (although it is not necessary to use the viewer to make the
mosaic). Note that the “results found” field is filled in the “Help” tab next
to the “Search Commands” field. It is automatically filled with the buttons
found from Step 1, and then the box for Step 2 in a figure points to the
“MosaicPro” button that is pressed.
1. In the “Help” tab, type “mosaic” into the “Search Commands” field, then
press “[Enter].”
2. In the “Help” tab, go to the “results found” group, then press the “Mosaic
Pro” button.
3. From the “MosaicPro (No File)” menu, Press “Edit,” then select “Add
Images …” from the dropdown.
4. In the “File name:” field of the “Add Images” menu, type “*.tif” then press
“[Enter].” In the same field, type a pathname, then press [Enter].
5. Select the desired filenames from the “File Chooser” menu, then press
“OK.”
Figure 2-14. Begin mosaic of LiDAR DEM raster.
(1) In the “Help” tab, type “mosaic” into the
“Search Commands” field, then press
“[Enter].”
(2) In the “Help” tab, go to the “results found”
group, then press the “Mosaic Pro” button.
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Figure 2-15. MosaicPro “Add Images...”
Press
Figure 2-16. Pick terrain heights elevation grids.
(4) In the “File name:” field of the “Add Images”
menu, type “*.tif” then press “[Enter].” In the same
field, type a pathname, then press [Enter].
(5) Select the desired filenames
from the “File Chooser” menu,
then press “OK.”
Figures 2-17 through 2-20 (Steps 6-7) show how to push an elevation grid
to the bottom of the list of “Image Names” that are shown at the bottom
subframe of the “Mosaic Pro” menu. Take these steps to ensure using
terrain heights of an elevation grid tile in areas where it overlaps other
DEM raster tiles. This is helpful, for example, to move tiles in the list so
that most recent terrain height values are put in the mosaic. Figures 2-18
through 2-20 (Steps 8-11) show how to run the LiDAR DEM terrain
heights mosaic, and Step 12 closes the Figure 2-17 menu.
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23
6. Select a filename to move the tile within the “Image Name” list (repeat
steps 6-7 for each tile moved in the list).
7. Press the “Send Selected Image(s) to Top” button.
8. From the (No files)” menu, click “Process,” then select
“Run Mosaic …”
9. In the “Output File Name” popup menu, type the output filename into the
“File name” field.
10. Press the “OK” button from the “Output Filename Menu” menu to run the
mosaic process.
11. After the “Process List” popup shows the “DONE” state indicating that the
mosaic is complete, press “Close.”
12. Once the mosaic process completes, close the “Mosaic Pro” menu by
pressing the “X” button in its upper right-hand corner of the menu. (This
step not shown within a figure, but it applies to Figure 2-17.)
Figure 2-17. Restack terrain height elevation grid tiles.
Press
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Figure 2-18. Run mosaic process.
press
Figure 2-19. Show output filename from mosaic process.
(10) Press the “OK” button from the “Output Filename
Menu” menu to run the mosaic process.
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Figure 2-20. Process list mosaic completion.
2.6
Adjust the terrain heights from the LiDAR DEM
Figures 2-21 through 2-23 show the LiDAR DEM raster mosaic of terrain
heights that must be converted into geographic latitude and longitude
units of decimal degrees, which are the units expected by the WorldView-2
NITF with RPC. Buckeye LiDAR DEM rasters have a UTM projection with
horizontal and vertical units of meters. The DEM raster in UTM meters
will produce inaccurate orthoimages unless they are converted to the
projection and datum expected by the WorldView-2 NITF with RPC first
by using the following steps. (Step 1 presumes that you already know how
to load images into the viewer.) The ERDAS Imagine image processing
software incorrectly assumes that the LiDAR datum is WGS84, where it
actually is EGM96. Steps 4-5 correct this misperception about the data
projection.
1. Load image of LiDAR DEM raster mosaic into the viewer.
2. Select the filename for the LiDAR DEM in the “Contents” subframe, then
press-RHMB for the selection to invoke the popup menu.
3. From the same popup menu, click “Metadata” button to invoke the “Image
Metadata” menu.
4. To change the incorrect WGS84 datum selection to EGM96Press, select
“Add/Change Elevation Info.”
5. From the “Datum Name” dropdown field, select “World Wide 15-Minute
Geoid (EGM96)” datum, then press “OK.”
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Figure 2-21. LiDAR terrain heights elevation mosaic.
(2) Select the filename for the LiDAR DEM in the “Contents”
subframe, then press-RHMB for the selection to invoke the popup
menu.
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27
Figure 2-22. Terrain heights elevation metadata.
Figure 2-23. Choosing vertical datum for terrain elevations.
press
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28
Figures 2-24 and 2-25 show how to acquire the “Image Metadata” for one
of the overlapping NITF MSI frames that the orthoimage mosaic will
include. The other overlapping but southeast WorldView-2 image frame
has similar metadata.
1.
2.
3.
4.
Select the MSI frame name. (It will highlight in blue.)
Press-RHMB the selection to invoke the popup list.
Select “Metadata” from the popup list.
The “Projection Info” for the MSI frame should be “WGS84 Geographic
(Lat/Lon).”
Figure 2-24. Review projection metadata for MSI.
(2) Press-RHMB the
selection to invoke the
popup list.
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Figure 2-25. MSI projection and datum.
Figures 2-26 through 2-32 (Steps 1-8) show how to convert the EGM96
terrain heights into WGS84 elevations. The same UTM projection with the
adjusted terrain heights will be converted to longitude and latitude in
decimal degrees afterwards for use with the RPC of the MSI so that the
WGS84 terrain heights will match both the datum and the projection
expected by the MSI RPC sensor model.
1. Select the terrain height elevation mosaic filename.
2. In the “Help” tab, place the cursor in the “Search Commands” Field, then
type “wgs.”
3. In the “Help” tab, go to the “results found” group, then press “Recalculate
Elevation Values.”
4. Press the “Define Output Elevation Info” button.
5. In the “Elevation Info Chooser” menu, select “WGS84” from the “Datum
Name” dropdown, then press “OK.”
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30
6. In the “Recalculate Elevation for Images” menu, click the folder icon next
to the Output File field.
7. In the “Output File” menu, Type in “File name” for the output converted
elevations, then press “OK.”
8. In the “Recalculate Elevations for Images” menu, press “OK.”
9. In the popup “Process List” menu, when the State is “DONE – Click Dismiss to Remove,” then press “Close.”
Figure 2-26. Recalculate terrain heights elevation values.
(2) In the “Help” tab, place the
cursor in the “Search Commands” Field, then type “wgs.”
press
.
Figure 2-27. Define output elevation vertical datum.
Press
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Figure 2-28. Project terrain heights from EGM96 to WGS84 vertical datum.
Figure 2-29. Convert WGS84 terrain heights from EGM96 to WGS84.
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Figure 2-30. Filename output for WGS84 terrain heights from EGM96.
press
Figure 2-31. Convert terrain heights from EGM96 to WGS84.
press
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Figure 2-32. Close processing list to complete conversion.
press
2.7
Convert LiDAR DEM from UTM to geographic decimal degrees
Figures 2-33 through 2-35 and Steps 1-9 show how to convert from the
UTM projection (with adjusted terrain heights) toward decimal degrees of
geographic longitude and latitude. The Output Filename within the yellow
box in Figure 2-34 is the input Elevation File for Step 5 in Figure 2-39.
1. In the “Help” tab, type “reproject” into the “Search Commands”
field.
2. In the “Results Displayed” group, press the blue “Reproject” button.
3. Enter the “Input File” and “Output File” names into the “Reproject Images” menu. (The input and output filenames will be different.)
4. Select “Geographic” and “Lat/Lon WGS84” from “Categories” and
“Projection” dropdown fields.
5. Check the “Ignore Zero In Stats” checkbox.
6. In the “Output Cell Sizes” group, press the “Nominal” button.
7. In the “Nominal Cell Sizes” menu, select 1x1 m, then click “OK.”
8. In the “Reproject Images” group, select the “Rigorous Transformation”
radio button.
9. Press “OK” to start the process.
10. In the popup “Process List” menu, when the State is “DONE – Press
Dismiss to Remove,” press “Close.”
Figure 2-33. Start projection of DEM raster from UTM to geographics.
(1) In the “Help” tab, type “reproject” into the
“Search Commands” field.
press
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Figure 2-34. Settings for reprojected images.
dem_1m_a1_mosaic_wgs_geo.img
Press
(7) In the “Nominal Cell Sizes” menu, select 1x1 m, then press
“OK.”
Press
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Figure 2-35. Close completed reprojection process.
(10) In the popup “Process List” menu, when the State is “DONE –
Press Dismiss to Remove,” press “Close.”
2.8
Making orthoimages
Figures 2-36 through 2-39 (Steps 1-5) show how to make the orthoimage
for each MSI frame of NITF with RPC. It is unnecessary to load a raw MSI
into the viewer to make its orthoimage. The Elevation Filename entered
within Step 5 of Figure 2-39 that is the same Output File name from Step 3
of the earlier Figure 2-34, where the filename is shown within the yellow
box in Figure 2-34 because was truncated within its shorter field there.
The “WorldView RPC Model...” menu will remain open after you press its
“Apply” button, so you can close it by pressing the “Close” button afterwards but subsequent processing will be unaffected.
1. In the “Help” tab, type “ortho” in the “Search Commands” field. In the
“results found” group, press the “Orthorectify without GCP.”
2. In the “Geo Correction Input File” menu, enter the “*.ntf” input filename,
then press “OK.”
3. From the “Set Geometric Model” menu, select “WorldView RPC,” then
click “OK.”
4. In the “WorldView RPC Model ...” menu, enter the “*.RPB” filename into
the “RPC File” field.
5. For “Elevation Source,” select the “File” radio button, enter the filename
into the “Elevation File” field, press “Apply,” then press “Close.”
Figure 2-36. Begin the orthoimage process.
(1)
ERDC/GRL TR-14-1
36
Figure 2-37. Pick NITF filename for making orthoimage.
press
Figure 2-38. Show RPC model.
press
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37
Figure 2-39. Show DEM raster.
press
After the “Resample” menu is applied then closed following Step 5, follow
the steps shown in Figure 2-40 (Steps 6-12) to start making the
orthoimage. Note that Steps 6-7 are not shown in Figure 2-40. These two
optional steps allow you to change the default output GSD suggested by
the software for each orthoimage. Skip Steps 6-7 to retain the output GSD
suggested by the software. Appendixes B and D include more detail about
choosing other output orthoimage GSD values. Use the same DEM raster
mosaic filename from Step 5 in Figure 2-39 for Step 10 in Figure 2-40.
6. (Optional) Press the “Feet/Meter Units” button in the “Resample” menu,
and check their values in the “Nominal Cell Sizes” popup.
7. (Optional) Put GSD values besides the ones shown into the “X” and “Y”
fields in the “Nominal Cell Sizes” menu to match the Elevation File sizes.
Press the “Apply” then “Close” buttons in the “Nominal Cell Sizes” popup.
8. Enter the “Output File” name into the “Resample” menu.
9. Check the “Snap pixel edges to” checkbox, then select the “raster image”
radio button.
10. Enter the same filename in the “Elevation File:” field into the “File to snap
to” field.
11. Check the “Ignore Zero in Stats” checkbox.
12. Click “OK” to start making the orthoimage.
13. In the popup “Process List” menu, when the State is “DONE – Click Dismiss to Remove,” press “Close.”
ERDC/GRL TR-14-1
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Figure 2-40. Start making the orthoimage.
(8) Enter the “Output File” name
into the “Resample” menu.
(9) Check the “Snap pixel edges to” checkbox,
then select the “raster image” radio button.
(10) Enter the same filename in the “Elevation
File:” field into the “File to snap to” field.
(11) Check the “Ignore
Zero in Stats” checkbox.
(12) Press “OK” to start
making the orthoimage.
Figure 2-41. Close completed process for making orthoimage.
press
Figures 2-42 and 2-43 show how poorly the MSI frames match each other
before making the orthoimages, and they show how the orthoimages
match afterward from making them separately, while all MSI frames are in
units of geographic longitude and latitude decimal degrees. It is worthwhile to compare the orthoimages where they overlap before converting
them to the UTM projection that has the same horizontal and vertical
units of meters. Note now how urban features besides buildings, say roads
in urban areas, match each other better geometrically where they overlap.
Use the “Swipe” tool to compare the MSI orthoimage with the DEM raster
image underneath them in the viewer.
Figure 2-42. Overlapping MSI before making orthoimages.
ERDC/GRL TR-14-1
39
Figure 2-43. Overlapping MSI after making both orthoimages.
ERDC/GRL TR-14-1
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2.9
Create UTM orthoimage
Figures 2-44 and 2-45 show how convert the geographic longitude and
latitude orthoimage into a UTM projection so that the MSI orthoimage
pixels will align with the LiDAR terrain heights elevation grid. It is unnecessary to load the orthoimage into the viewer to convert it to UTM. The
software automatically puts the “Reproject” button into the “results found”
field of the “Help” tab from Step 1. Press the “Reproject” button (identified
by its blue crescent shadow). In Step 2, the input filename is for the
orthoimage in a geographic longitude and latitude projection; the output
filename is for the orthoimage converted to the UTM projection. This
allows horizontal distance measurements in meters on the orthoimage
mosaic because of its new UTM projection.
1. On the “Help” tab, enter “reproject” in the “Search Commands” field, then
press the “Reproject” button.
2. In the “Reproject Images” menu, enter both input and output filenames
into their respective fields.
3. Select “UTM WGS84 North” from the “Categories” filenames for
dropdown menu.
4. Select “UTM Zone 41 (Range 60E – 66E)” from the “Projection” field
dropdown.
5. Select “Meters” from the “Units” field dropdown.
6. Check the “Ignore Zero in Stats” checkbox.
7. Select “Rigorous Transformation” choice.
8. Press the “OK” button to start the process.
9. Select the “Rigorous Transformation” radio button.
10. Press “OK” to start the process.
11. In the popup “Process List” menu, when the State is “DONE – Click Dismiss to Remove,” press “Close.”
41
ERDC/GRL TR-14-1
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Figure 2-44. Resample geographic MSI orthoimage to UTM projection.
press
ERDC/GRL TR-14-1
43
Figure 2-45. Reproject images menu.
M
M
Press
ERDC/GRL TR-14-1
44
Figure 2-46. Processing list reproject progress.
press
Figure 2-47 shows how well a completed false color orthoimage with its
UTM projection aligns with the image of the LiDAR DEM raster. It is now
possible to expand the area covered by MSI by adding all separately produced orthoimages to a mosaic of adjacent and overlapping orthoimages,
where the mosaic has the same GSD throughout.
Figure 2-47. Orthoimage alignment with LiDAR terrain heights image.
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3
Conclusions and Recommendations
3.1
Conclusions
This tutorial described procedures for making orthoimages from LiDAR
DEM and from commercial satellite MSI in the NITF with RPF.
Orthoimages rectify digital imagery to remove geometric distortions that
are caused by the varying elevations of the exposed terrain features, and by
the exterior and interior orientations of the sensor. When individual
orthoimages are combined, the resulting mosaic covers a wider area for
image-maps and it contains less visible seams where the orthoimages
overlap, making the user more comfortable when reviewing image-map
products. The processes in this tutorial will enhance the production of
orthoimage mosaics used by the Army Geospatial Center and ERDC-GRL
as image-map products that aid tactical decisions and improve situational
awareness.
The procedures described for making orthoimages also should work when
combining other DEM and MSI data with different resolutions. Other
DEM that lack the spatial resolution of LiDAR may include Digital Terrain
Elevation Data (DTED) for example, global Shuttle Radar Topography
Mission (SRTM), Interferometric Synthetic Aperture Radar (IFSAR), or
the terrain elevations from stereo correlation of the original MSI or its
panchromatic band that has one-quarter of the GSD for the MSI bands.
The LiDAR is preferred, however, because of its better spatial resolution to
improve the registration of DEM and MSI data (and to avoid the stereo
correlation for terrain elevations with the overlapping panchromatic
bands) for example in urban areas with many elevation discontinuities
from manmade structures.
3.2
Recommendations
To better process higher resolution images, it is recommended that future
research reassess current practices of feature extraction from the LiDAR
DEM raster and commercial satellite MSI data that are combined in the
process of making orthoimages, where feature extraction for models of
surface material in urban areas will improve from the better spatial resolution of LiDAR and the orthoimages produced from it.
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ERDC/GRL TR-14-1
The process outlined in this tutorial described two overlapping, but separately produced orthoimages, but it ignored tonal imbalance and output
GSD discrepancies between orthoimages in a mosaic. It is recommended
that future research investigate methods to remove such geometric discrepancies, including tonal imbalance between both orthoimages.
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References
Agouris, P., Doucette, P. & Stefanidis, A., 2004. Automation and Digital Photogrammetric
Workstations. In: J. C. McGlone, E. M. Mikhail, J. Bethel & R. Mullen, eds.
Manual of Photogrammetry. 5th ed. Bethesda, MD: American Society for
Photogrammetry and Remote Sensing (ASPRS), pp. 949-981.
Brown, R. O., 2013. “Fusing Terrain Elevations into Sensor Imagery.” Baltimore 2013
ASPRS Annual Conference Proceedings. Bethesda, MD, Baltimore 2013 ASPRS
Annual Conference Proceedings.
Campbell, J. B. & Wynne, R. H., 2011. Image Classification. In: Introduction to Remote
Sensing. 5th ed. New York, NY: The Guilford Press, pp. 335-381.
Forstner, W. & Wrobel, B. P., 2013. Mathematical Concepts in Photogrammetry. In: J. C.
McGlone & G. Y. G. Lee, eds. Manual of Photogrammetry. 6th ed. Bethesda, MD:
American Society of Photogrammetry and Remote Sensing (ASPRS), pp. 63-233.
Forstner, W. et al., 2013. Analytic Photogrammetric Operations: Replacement Sensor
Models. In: J. C. McGlone & G. Y. G. Lee, eds. Manual of Photogrammetry.
Bethesda, MD: American Society of Photogrammetry and Remote Sensing
(ASPRS), pp. 891-940.
Homer, C. et al., 2004. Development of a 2001 National Land-Cover Database for the
United States. Photogrammetric Engineering & Remote Sensing, July, 70(1), pp.
829-840.
HQDA, 2001. Map Reading and Land Navigation. In: Army Field Manual. Washington
DC: Department of the Army.
HQUSACE, 2002. Photogrammetric Mapping: Engineering and Design. Washington
DC: Department of the Army.
Hu, Y., Tao, V. & Croitoru, A., 2004. Understanding the rational function model:
methods and applications. s.l., Organising Committee of the XXth international
congress for photogrammetry and remote sensing, pp. 663-668.
Jacobsen, K., 2008. Geometric Modelling of linear CCDs and panoramic imagers. In: Z.
Li & E. Baltsavias, eds. Remote Sensing and Spatial Information Science.
London: Taylor and Francis Group Press, pp. 145-155.
LANDinfo Worldwide Mapping, 2014. WorldView-2 High-Resolution Satellite Imagery:
Specifications & Pricing. [Online]
Available at: <http://www.landinfo.com/WorldView2.htm>
Lemoine, E. G. et al., 2004. EGM96: The NASA GSFC and NIMA Joint Geopotential
Model, Greenbelt MD: NASA Goddard Space Flight Center.
Longbotham, N. et al., 2012. Very High Resolution Multiangle Urban Classification
Analysis. IEEE Transactions, April, 50(4), pp. 1155-1170.
48
ERDC/GRL TR-14-1
Miller, S., 2013. Photogrammetric Products. In: J. C. McGlone & G. Y. G. Lee, eds.
Manual of Photogrammetry. 6th ed. Bethesda, MD: American Society for
Photogrammetry and Remote Sensing (ASPRS), pp. 1009-1043.
Mugnier, C. J. et al., 2013. The Mathematics of Photogrammetry. In: J. C. McGlone & G.
Y. G. Lee, eds. Manual of Photogrammetry. 6th ed. Bethesda, MD: American
Society of Photogrammetry and Remote Sensing (ASPRS), pp. 235-357.
NITF Standard Technical Board, 2007. Table of Contents. [Online]
Available at: <http://www.gwg.nga.mil/ntb/baseline/toc.html>
Pack, R. T. et al., 2012. In: M. S. Renslaw, ed. Manual of Airborne Topographic LiDAR.
Bethesda, MD: American Society for Photogrammetry and Remote Sensing
(ASPRS), pp. 7-98.
Satellite Imaging Corporation, 2013. WorldView-2 Satellite Sensor. [Online]
Available at: <http://www.satimagingcorp.com/satellite-sensors/worldview2.html>
Scarpace, F., 2013. Elements in Orthophoto Production - Webinar. s.l.:American Society
for Photogrammetry and Remote Sensing (ASPRS).
Tao, C. V. & Hu, Y., 2001. A Comprehensive Study on the Rational Function Model for
Photogrammetric Processing. Photogrammetric Engineering and Remote
Sensing, December, 67(12), pp. 1347-1357.
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Acronyms and Abbreviations
Term
Definition
AGC
U.S. Army Geospatial Center
AGC-GRL
U.S. Army Geospatial Center – Geospatial Research Laboratory
ASPRS
American Society for Photogrammetry and Remote Sensing
CCD
Charge-Coupled Device
CEERD
U.S. Army Corps of Engineers, Engineer Research and Development Center
DEM
Digital Elevation Model
DTED
Digital Terrain Elevation Data
EGM96
Earth Gravity Model 1996
EM
Engineer Manual
ERDC
Engineer Research and Development Center
ERDC-GRL
Engineer Research and Development Center-Geospatial Research Laboratory
FM
Field Manual
GCP
Ground Control Point
GSD
Ground Sample Distance
GSFC
Goddard Space Flight Center
HQDA
Headquarters, Department of the Army
HQUSACE
Headquarters, U.S. Army Corps of Engineers
IEEE
Institute of Electrical and Electronics Engineers
IFSAR
Interferometric Synthetic Aperture Radar
LiDAR
Light Detection and Ranging
LLC
Limited Liability Company
MSI
Multi-Spectral Imagery
NASA
National Aeronautics and Space Administration
NIMA
National Imagery and Mapping Agency
NITF
National Imagery Transmission Format
RPC
Rational Polynomial Coefficients
SF
Standard Form
SRTM
Shuttle Radar Topography Mission
TIFF
Tagged Image File Format
TR
Technical Report
U.S.
United States
USACE
U.S. Army Corps of Engineers
UTM
Universal Transverse Mercator
WGS84
World Geodetic System 1984
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Appendix A: LiDAR DEM Raster
Figure A-1 shows the processing of a single LiDAR laser shot (commonly
called a “pulse”) to find points along the recorded waveform of its reflected
return (Pack, et al., 2012). This process usually finds peaks along the
waveform that show a strong returned laser signal reflected from a relatively solid terrain surface or subsurface for the entire time along its range
of the laser shot. Each waveform measures and records the continuous
strength of the reflected return throughout the time interval when each
laser is shot from the sensor and then reflected back toward it.
Figure A-1. Motivation for initial step of waveform post-processing.
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This process forms a three-dimensional cloud of points, each with a returned value for the overall returned strength of the signal. The X, Y, and Z
value for each point in the cloud is determined relative to the known exterior and interior orientation of LiDAR sensor, at given times, along the
entire waveform. This point cloud is sampled to produce a uniform raster
for the DEM that is used when making orthoimages. The first peak along
each waveform becomes an elevation value for the reflective surface of the
terrain skin. These points are converted to a uniform grid of X, Y, Zelevation values that form the DEM raster. This DEM raster of terrain
heights from the first return is used when making an orthoimage.
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Appendix B: Orthoimage Spatial Resolution
This appendix describes options for producing orthoimages and the impacts on the orthoimage if one uses output spatial resolutions other the
nominal GSD of the raw MSI suggested by the software. For example, one
might wish to specify a consistent output spatial resolution between each
separately produced orthoimages that are combined together in a mosaic.
The nominal dimensions for the raw MSI are expressed as the GSD for the
width and length of each MSI pixel footprint on the terrain. The spatial
resolution for the DEM raster grid-cell is expressed as the GSD in meters
for the UTM projection. The DEM also has a spatial resolution different
from that of the raw MSI. These different spatial resolutions are important
because an orthoimage is constructed by finding the unknown nearest
pixel in the raw MSI given the known X, Y, and the estimated Z value that
is the terrain height found for the DEM raster cell.
Producing an orthoimage with the same output spatial resolution as the
DEM is a practical way to register both datasets together for two reasons:
1. It will be unnecessary to estimate terrain height values by interpolating
from the DEM when producing the orthoimage, which allows the use of
the terrain heights directly from the DEM when producing the orthoimage
with the ground-to-image sensor model.
2. The produced orthoimage will have a single terrain height value associated
with each orthoimage grid-cell along with a vector of brightness values
from each layer of the MSI, so that additional channels derived from the
DEM raster can serve as extra layers during MSI feature extraction.
The orthoimage also will contain rectangular or square pixels with a shape
different from that of raw MSI pixel footprints on the ground, which will
more nearly approximate the shape of a parallelogram. The array of MSI
pixels also is oriented differently than the orthoimage pixels. An
orthoimage has pixels oriented in the x-east and y-north directions, but
the MSI pixel footprints are shaped by the orbital mechanics of the satellite platform and by the perspective of a push-broom scanner for
WorldView-2 MSI. All of these concerns complicate conversions between
the raw MSI and the orthoimage spaces. These differences between the
shape and orientation of raw MSI pixel footprints on the terrain, relative
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to the output orthoimage GSD, mean that the raw MSI requires
resampling regardless of how close its nominal pixel footprint size is to
output GSD for the produced orthoimage.
One must determine the output spatial resolution of the produced
orthoimage to resolve issues involving the shape and size differences
between the pixel footprints for separate frames of raw MSI, and for the
different DEM raster shape/size. Although choosing a single output spatial
resolution can make it difficult to retain the approximate nominal spatial
resolution for each raw MSI frame, each separately produced orthoimage
in a mosaic should have the same spatial resolution to prevent resampling
when the orthoimages are combined into a mosaic.
However, the metadata for the MSI might include ranges of nominal GSD
for the terrain footprint of its pixels. This information helps the software
or user to decide the overall output spatial resolution for each separately
produced orthoimage. Choices regarding the anticipated output spatial
resolution of orthoimages must be made within the datum and projection
of the final product required by the customer. This appendix provides
guidance on choosing a common GSD for all orthoimages that are to be
combined into a single mosaic.
Table B-1 lists the actual dimensions of pixel footprints on the ground for
each overlapping MSI frame and of the LiDAR DEM raster for the material
used in this tutorial. Values listed in Table B-1 were from the metadata for
the MSI and the DEM. Note that the nominal GSD for the raw MSI is a
range of values that reflect the fact that the MSI pixel GSD dimensions
vary throughout its entire frame regardless of the terrain flatness. This
complicates the decision that the software (or user) must make to determine the best single output resolution for each orthoimage in the mosaic.
54
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55
Table B-1. DEM and MSI pixel dimensions.
Table B-1 lists a range of values for the nominal GSD for each pixel footprint. The size of each pixel footprint will differ even if the entire image is
exposed instantaneously. The scale of each pixel is the distance from the
focal point to the point on the focal plane divided by the range from the
focal point to the spot in the DEM space; this affects the size of each pixel
footprint that depends on elevation values that change across the entire
image.
The shape of each MSI pixel footprint projected onto the terrain is nearly a
parallelogram because its shape is affected by the exterior orientations of
the sensor. These orientations are something other than 90-degree angles
between the “along” and “across” scan directions of the sensor carried by
the satellite platform. Consequently, the mathematics and projective
geometry of photogrammetry, plus the terrain heights, affect the size and
shape for each MSI pixel footprint differently. This accounts for the range
of GSD and the lack of a single spatial resolution throughout the MSI
frames (Forstner & Wrobel, 2013; Mugnier, et al., 2013).
ERDC/GRL TR-14-1
It is unclear how the image processing software sets the output GSD when
making the orthoimage given the range of nominal resolution for the MSI,
in that it still needs to estimate Z values of terrain heights for other spots
besides those of the current DEM raster. Although this tutorial ignores
concerns about best methods to resample DEM rasters while making
orthoimages, the default value for the resolution of the output orthoimage
can, if desired, be changed to the resolution of the DEM, or to another
common GSD for all of the separately produced orthoimages. This Appendix describes the resulting difference in one of the output orthoimages in
which values chosen automatically by the software are:
• DEM GSD=1.0 m compared to a nominal MSI pixel footprint
• GSD=3.05 m in the x-east direction
• GSD=equals 2.61 m in the y-north direction.
Note that the nominal average GSD of the pixel footprints was 2.514 m for
one MSI frame, and 2.242 m for the other MSI frame, because the MSI
metadata provides this as a range of GSD values.
Figure B-1 shows a split view of the pixel footprint shape for the raw MSI
and the orthoimage produced from it. Note that, on the left side of the
viewer, the raw MSI pixel footprints are parallelograms. On the right side
of the viewer, the orthoimage pixels are rectangles oriented perpendicularly in the x-east and y-north directions. However, the raw MSI pixel footprints are parallelograms oriented with respect to the along-track of the
satellite path, and to the across-track scan direction of the satellite, where
the orientation of the MSI raster is affected by satellite orbital mechanics.
Moreover, the NITF metadata implies that these along-flight and alongscan directions are slightly other than perpendicular to each other. These
parallelograms are square pixels in the sensor array that are reshaped by
the exterior orientation of the sensor when they are projected to form their
footprint on the terrain. The split view also shows the offsets of imageobjects between the raw MSI and its orthoimage that are produced with a
GSD suggested by the default values of the software. Tonal imbalance
remains between the split views, but this makes it easier to distinguish
between each image shown in the viewer.
56
Figure B-1. MSI and orthoimage equivalent spatial resolutions.
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57
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58
Figure B-2 shows the size of pixel footprints from the metadata for the
produced orthoimage on the right side of Figure B-1, where the software
automatically determines the suggested default output values that reflect
the GSD spatial resolution, which is a square pixel footprint with units of
decimal degrees.
Figure B-2. Default orthoimage pixel GSD In decimal degrees.
Figure B-3 shows the size of the orthoimage pixels in meters. This size
reflects the nominal GSD from the raw MSI suggested by the software,
which is a rectangular (instead of square) pixel width and length by meters
in two perpendicular directions of east and north. The different shapes
and orientations between the lattices of the raw MSI pixel footprints and
the orthoimage pixels necessitate a resampling of the raw MSI in the
orthoimage. This resampling is needed regardless of the chosen GSD in
decimal degrees or meters, if only because the pixels differ in shape and
orientation between the raw MSI, and in their range of GSD values in both
directions compared to the orthoimage.
ERDC/GRL TR-14-1
59
Figure B-3. Default orthoimage pixel GSD in meters.
To explain the discrepancy, the meters of GSD for each direction (north or
east) in the orthoimage are determined by different meters-per-angle
increments on parallels compared with meridians in an ellipsoid-ofrevolution. So the pixel is square with equal angular increments for longitude and latitude. However, the pixel becomes a rectangular with unequal
dimensions in meters when comparing the east and north directions regardless of the projection. This implies that the spectral properties of the
original imagery are perturbed by resampling without regard for how close
the orthoimage output spatial resolution is to the nominal GSD of the raw
MSI, or from the differences between the orientations of the footprint for
the MSI coordinate frame and that of the produced orthoimage.
Each orthoimage pixel includes more than one terrain elevation value
from the DEM given the dimensions used to best retain the spatial resolution of the raw MSI, unless the output pixel GSD for the orthoimage is
reset at 1 m to match the spatial resolution of the DEM, which will result in
one terrain elevation value for each orthoimage pixel. MSI feature extraction literature describes the advantages of having a single elevation value
for each orthoimage pixel (Brown, 2013; Campbell & Wynne, 2011;
Homer, et al., 2004; Longbotham, et al., 2012).
ERDC/GRL TR-14-1
60
Figure B-4 shows the four corners of the entire MSI frame projected onto
the terrain surface. This reflects the overall nearly parallelogram shape for
the footprint of the entire MSI frame in the UTM coordinate system
(Figure 2-13, p 20), and shows that the shape of each pixel matches that of
the entire MSI frame.
Figure B-4. Raw MSI corners from NITF.
Figure B-5 shows the angles that reflect the satellite orbital mechanics,
along its track velocity and across its track scan velocity, given its obliquity
angle between the satellite nadir and vertical vector that is perpendicular
to the terrain surface of the rotating earth. These angles cause the parallelogram shape of the frame and its pixels.
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61
Figure B-5. Sensor orientation metadata from NITF.
Figure B-6 shows the produced orthoimage that is reset to equal the 1 m
precision of the DEM raster, because the output resolution was reset to be
1 m in both of the X-east and Y-north directions. This was different from
the default output resolutions for the orthoimage suggested by the software. More geometric distortions are removed from the raw MSI, however,
when the better resolution of the DEM is fully used, compared to the
orthorectification that only retains the nominal GSD of the raw MSI.
It is uncertain how the software estimates a terrain height when the X and
Y position within the DEM is between actual posts within its raster. This
might compromise the geometrical integrity of the output orthoimage
when there are many elevation discontinuities between the posts with a
terrain height, for example from buildings in an urban zone. One concerned with retaining the spectral integrity of the raw MSI should create a
feature layer directly from the raw MSI, and then convert the thematic
raster to an orthoimage. A separate process was developed to orthorectify
the theme map from feature extraction in the raw MSI. (This is described
elsewhere.)
Figure B-6. 1 m GSD Orthoimage Compared To 1 m GSD DEM Raster.
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62
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Figure B-7 and B-8 show the first three bands of the raw MSI pixel footprints from the first MSI frame (without the orthorectification process
applied to it). Figure B-7 shows the DEM raster terrain height values posts
overlayed onto the frame. Figure B-8 shows its produced orthoimage
footprints in which the output spatial resolution suggested by the software
is close to the nominal GSD of the pixel footprint (a parallelogram-like
shape) from the raw MSI with the nominal average GSD (x:2.63 m by
y:3.08 m). Figure B-9 shows the orthoimage produced with the same
spatial resolution of the DEM raster (1 m x 1 m). This means that each
pixel for the orthoimage with the default pixel dimensions contains 2-or-3
DEM spot values in the east-x-direction, and 3-or-4 DEM spot values in
the north-y-direction. Each “pixel’s worth” of data from the orthoimage
contains 6, 8, 9, or 12 DEM spots within the orthoimage that has the same
spatial resolution suggested by the software.
The left side of Figure B-9 shows the result, within the viewer, of having an
output spatial resolution for the orthoimage that matches the GSD of the
DEM raster, alongside the orthoimage (shown on the right side of the
viewer) that has the nominal spatial resolution of the raw MSI. Note that
the edges of buildings are smoother when the orthoimage uses the better
spatial resolution of the DEM raster. There also is one terrain height DEM
value for each pixel of the orthoimage when they become registered together through the orthorectification process, and when the output spatial
resolution of the orthoimage matches the GSD for the DEM.
This implies more geometrical rectification within the orthoimage that
retains the GSD of the DEM with better resolution than the raw MSI. A
single elevation value for each orthoimage pixel results from projecting
each DEM post into the raw MSI space, so there is one DEM terrain height
value for each orthoimage pixel. This makes it possible to add terrain
heights, or derivatives such as slope and aspect, as extra channels or layers
into the MSI to enhance feature extraction, because the features are automatically registered to the orthoimage through the process of producing it.
If the full resolution of the DEM is used in the orthorectification process,
each orthoimage pixel is the result of one known elevation value.
63
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64
Figure B-7. Raw MSI and DEM raster terrain height posts.
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65
Figure B-8. Orthoimage with nominal GSD of raw MSI and DEM posts.
Figure B-9. 1 m GSD orthoimage compared x:3.09 by y:2.63 GSD orthoimage.
ERDC/GRL TR-14-1
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Chapter 2, “Procedures,” sought to clarify the output spatial resolution by
using the values suggested by software. However, this appendix section
has further explained some important spatial and spectral concerns that
should be considered when choosing the output spatial resolution of the
produced orthimages, described as optional steps in Chapter 2 (p 37).
Specifically, this appendix has explained why the output spatial resolution
of the orthoimage could be something besides the nominal GSD of the raw
MSI suggested by the software. The geometric accuracy of each
orthoimage and of the mosaic also might be compromised if the nominal
GSD of the raw MSI suggested by the software is used instead of some
other specified value.
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68
Appendix C: RPC Projective Equations
Equations C-1 and C-2 show how current commercial image processing
software projects each DEM grid-cell with a terrain elevation value 𝒛 from
the ground into the MSI raster by using the RPC (Tao & Hu, 2001). This
replaces the actual sensor model to produce new orthoimages with distortions removed from its raw image. This pair of equations forms two quotients, each with a ratio of two polynomials called rational functions for
multiple variables, such as DEM raster grid cells (𝒙, 𝒚, 𝒛) transformed to
pixels in image space (𝒙′ , 𝒚′).
[𝒙, 𝒚, 𝒛]𝑫𝑬𝑴 → [𝒙′ , 𝒚′]𝑴𝑺𝑰 :
𝒙′ =
where:
𝒚′ =
𝑷(𝒙,𝒚,𝒛)
𝑸(𝒙,𝒚,𝒛)
𝑹(𝒙,𝒚,𝒛)
𝑺(𝒙,𝒚,𝒛)
=
=
∑𝒌 ∑𝒋 ∑𝒊�𝒂𝒊𝒋𝒌 � �𝒙𝒊 ��𝒚𝒋 ��𝒛𝒌 �
𝒑
∑𝒌 ∑𝒋 ∑𝒊�𝒂𝒊𝒋𝒌 � �𝒙𝒊 ��𝒚𝒋 ��𝒛𝒌 �
𝒒
∑𝒌 ∑𝒋 ∑𝒊�𝒂𝒊𝒋𝒌 � �𝒙𝒊 ��𝒚𝒋 ��𝒛𝒌 �
(C-1)
𝒓
∑𝒌 ∑𝒋 ∑𝒊�𝒂𝒊𝒋𝒌 � �𝒙𝒊 ��𝒚𝒋 ��𝒛𝒌 �
𝒔
𝒂𝒊𝒋𝒌 = the RPC delivered along with the MSI [for example the (α221)p
coefficient is for the (𝒂𝟐𝟐𝟏 )𝒑 (𝒙𝟐 )(𝒚𝟐 )(𝒛𝟏 ) term in the
numerator for the rational polynomial].
P and R = functions in the numerators for the equations
Q and S = functions in the denominators for the equations
P, Q, R, and S each have a different set of coefficients, say {P:(aijk)p} for
example, where there is a different coefficient for each product
of 𝒙 raised to the 𝒊𝒕𝒉 power, 𝒚 raised to the 𝒋𝒕𝒉 power, and 𝒛
raised to the 𝒌𝒕𝒉 power.
Equations C-2 show how the [𝒙, 𝒚, 𝒛] coordinates of the DEM space and the
[𝒙′ , 𝒚′] coordinates of the MSI space are actually offsets from each of the
defined perspective center of both spaces to normalize the RPC model.
These centers of each space are contained in the NITF along with the RPC.
Consequently, these coordinates are components of the projective vector
from DEM space into MSI space.
𝑥 = x − x0
𝑦 = y − y0
𝑧 = z − z0
𝑥 ′ = x ′ − x0′
𝑦 ′ = y ′ − y0′
(C-2)
ERDC/GRL TR-14-1
Appendix D: Orthoimage Mosaic
An orthoimage mosaic is produced similarly to the way that a LiDAR DEM
mosaic was created in Section 2.3 of the Chapter 2, “Procedures,” except
that each separately produced orthoimage within the mosaic is listed
instead of each LiDAR DEM shown in Section 2.3.
It is sufficient to understand Section 2.3 of the tutorial to determine the
necessary processing steps for combining the MSI orthoimages into a
single mosaic, if one simply generalizes the process to create a mosaic
from orthoimages instead of DEM rasters. If each orthorectification has a
different output spatial resolution, a choice still needs to be made about
the overall GSD of the orthoimage mosaic. This will result in another
sampling of each orthoimage that does not have the same GSD as the
output mosaic.
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70
Appendix E: Earth Gravity Model Terrain
Heights
Figures E-1 and E-2 show an example of the WGS84 ellipsoid and EGM96
geoid height at the same UTM coordinates of 627425.68 m east and
3498150.28 m north(Lemoine, et al., 2004). The difference between the
elevations for each of the two vertical datums (shown in the “FILE PIXEL”
field) indicates that the geoid is about 24 m below the ellipsoid (776.652 m
minus 752.362 m = 24.290 m). Repeated measurements across the entire
DEM show a slight variance for this elevation difference of less than 1 m
because the geoid values vary slightly throughout the entire DEM. This
approximately 24 m of vertical error within the DEM will propagate
throughout the RPC DEM-to-MSI projection unless the terrain heights are
converted from EGM96 to WGS84 before making the orthoimage.
Figure E-1. WGS84 ellipsoid height.
Figure E-2. EGM96 geoid height.
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4. TITLE AND SUBTITLE
Creating Orthographically Rectified Satellite Multi-Spectral Imagery with High Resolution Digital
Elevation Model from LiDAR:
A Tutorial
3. DATES COVERED (From - To)
5a. CONTRACT NUMBER
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5c. PROGRAM ELEMENT
6. AUTHOR(S)
Roger O. Brown
5d. PROJECT NUMBER
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5f. WORK UNIT NUMBER
7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)
US Army Engineer Research and Development Center (ERDC)
Geospatial Research Laboratory
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ERDC/GRL TR-14-1
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13. SUPPLEMENTARY NOTES
14. ABSTRACT
Orthoimages are used to produce image-map products for navigation and planning, and serve as source data for advanced research, development, testing, and evaluation of feature extraction methods. This tutorial describes procedures for making orthoimages from Light
Detection and Ranging (LiDAR) Digital Elevation Models (DEM) and from commercial satellite Multi-Spectral Imagery (MSI) in the
National Imagery Transmission Format (NITF) with Rational Polynomial Coefficients (RPC). Orthoimages rectify digital imagery to
remove geometric distortions caused by the varying elevations of the exposed terrain features, and by exterior and interior orientations
of the sensor. When orthoimages are combined, the resulting mosaic covers a wider area and contains less visible seams, which makes
the map easier to understand. RPC replace the actual sensor model while processing the original MSI. This generic replacement sensor
model is provided with the distributed imagery to simplify the process of removing the geometric distortions so image processing
software can create orthoimages without using the actual sensor model, which is often not provided. The DEM and MSI also become
better registered together after producing the orthoimage by using the RPC. This assists feature extraction and segmentation when the
DEM is added as extra data bands to the MSI.
15. SUBJECT TERMS
LiDAR, satellite imaging, aerial imagery, orthoimages, remote sensing
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